The proportion of positive test results most affects which metric?

• A. Negative Predictive Value
• B. Prevalence of disease
• C. Proportion of positive tests
• D. Specificity

Answer: (C)

The main idea is how the positivity rate—the proportion of people who test positive—depends on both how common the disease is and how the test performs. If disease prevalence goes up, more true positives appear; if a test has high sensitivity, it catches more of those cases; if specificity is lower, more false positives enter the positive results. All of these factors directly influence how many tests come back positive.

So the metric that is most directly shaped by the proportion of positive results is the proportion of positive tests itself. It is the actual observed measure you're trying to describe, and it changes when prevalence or test performance changes. The other metrics relate to this rate in more indirect ways: prevalence is the underlying disease frequency that helps determine the positivity rate, while negative predictive value and specificity relate to how negative results are interpreted and to the rate of false positives, respectively.